On improving efficiency of SLIQ decision tree algorithm

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 66-71

  Chandra, B ^a   Varghese, P Paul ^a,b  

^a INDIAN INSTITUTE OF TECHNOLOGY DELHI   (India)

^b NIRMA UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BOOLEAN FUNCTIONS; COMPUTER NETWORKS; DECISION SUPPORT SYSTEMS; DECISION THEORY; DECISION TREES; FINANCIAL DATA PROCESSING; INFORMATION MANAGEMENT; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS;

DECISION-TREE ALGORITHMS; JOINT CONFERENCE;

TREES (MATHEMATICS);

EID: 51749121785     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4370932     Document Type: Conference Paper

References (7)

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2. M. Mehta, R. Agrawal, and J. Riassnen., "SLIQ: A fast scalable classifier for data mining," in Extending Database Technology, Avignon, France: Springer, 1996,pp 18-32.
3. R. Rastogi and K. Shim. "Public: A decision tree classifier that integrates building and pruning," In Proc. of 24th International Conference on Very Large Data Bases, New York, 1998, pp 404-415.
4. J.C.Shafer, R. Agarwal and M. Mehta, "SPRINT: A scalable parallel classifier for data mining", Proceedings of the 24th International Conference on Very large Databases, Mumbai, India September 1996.
5. J.R. Quinlan, Introduction of decision tree. Machine Learning,1:81-106, 1986.
6. J.R. Quinlan, C4.5: Programs for Machine Learning. San Mateo, California: Morgan Kaufmann, 1993.
7. Quinlan, J. R, Improved use of continuous attributes in C4.5, Journal of Artificial Intelligence Research 4, 1996, pp. 77-90.